Emergent Research

EMERGENT RESEARCH is focused on better understanding the small business sector of the US and global economy.

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The authors are Steve King and Carolyn Ockels. Steve and Carolyn are partners at Emergent Research and Senior Fellows at the Society for New Communications Research. Carolyn is leading the coworking study and Steve is a member of the project team.

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Assistant Edge

November 14, 2017

Using Big Data and Machine Learning to Improve Small Business Lending

Intuit recently announced QuickBooks Capital, a new small business lending product that provides users of QuickBooks access to small business loans up to about $35,000.

This new service's lending process is done algorithmically and from within QuickBooks itself. Thanks to big data and machine learning techniques, most borrowers will know whether or not they are approved for a loan in just a a few minutes.

Lack of credit is consistently one of the top challenges for small businesses. And for new businesses (those that have been in business less than 5 years), the challenges are even greater.

According to the Federal Reserve of New York 2016 Credit Survey: Report on Startup Firms, 70% of young businesses say they need funding to grow. But only 23% of these firms are successful at getting all the funding they are looking for.

The main reason these firms struggle is insufficient credit history.

Simply put, they haven't been around long enough to establish a strong enough track record for lenders to be comfortable providing them credit.

And even if they get the credit they're looking for, small business satisfaction with the lending process is not good

Only 48% of successful borrowers report being satisfied with their experience with small banks, 31% with big banks and 23% with online lenders.

Intuit hopes to improve on all these metrics.

The QuickBooks Connect credit model is, not surprisingly, based on QuickBooks Online data.

Intuit has over 2 million small businesses that have agreed to allow Intuit to use their anonymized QuickBooks data in an aggregated format to develop products like QuickBooks Capital.

This add ups to over 28 billion data points Intuit can use in its credit model.

(An important side point is under Intuit's user agreement, QuickBooks users own their data and have to expressly provide Intuit permission to use their data in this manner).

This database of income statement, balance sheet, cash flow and transactions data allows Intuit to fully analyze the current financial state of a small business and predict its ability to pay back a loan.

It also means Intuit's credit model has enough data to allow them to lend to young small businesses - even those that have been around less than one year.

Based on the service's beta customers, this approach is broadening credit availability and making it easier and quicker to get a loan.

According to an analysis by Intuit of its early borrowers:

46 percent of QuickBooks Capital customers had never applied for a loan before.

60 percent of QuickBooks Capital customers would likely not get a loan elsewhere.

90 percent of QuickBooks Capital customers say the loan helped their business grow.

Almost all borrowers reported being satisfied with the process.

Everyone likes to point out that small businesses are "the engine that drives America's economy".

While this is a bit of an overstatement, it is true they are important drivers of our economy.

Making capital more available and easier to access - especially for young small businesses - will clearly increase economic growth.

QuickBooks Capital is also a good example of how small businesses are going to take advantage of big data, AI and machine learning.

In most cases, small businesses will leverage 3rd party products and services to utilize these advanced techniques.